@@ -24,7 +22,7 @@ In our project we want to use the concept of human needs to better analyze text
In [Paul&Frank(2019)](https://www.aclweb.org/anthology/N19-1368/) this approach is already taken up. Here, the two authors pursue the question why certain positions, opinions and views on a topic are obtained by the author. The authors see the underlying reason in the concepts of human needs. Maslow(1943) and Reiss(2004) already deal with human needs. Here Maslow sets up the so-called Maslow's hierarchy of needs.
> Maslow's hierarchy of needs is a motivational theory in psychology comprising a five-tier model of human needs, often depicted as hierarchical levels within a pyramid. From the bottom of the hierarchy upwards, the needs are: physiological (food and clothing), safety (job security), love and belonging needs (friendship), esteem, and self-actualization. ~ quoted from [here](https://www.simplypsychology.org/maslow.html#gsc.tab=0)
Reiss takes up this pyramid of Maslow and expands it with further, more profound motifs, matching the respective categories (see picture below). <br>
Reiss takes up this pyramid of Maslow and expands it with further, more profound motives, matching the respective categories (see picture below). <br>
Based on Maslow's pyramid and looking at the concepts from [ConceptNet](https://conceptnet.io/), we can then target the assignment and analyze and evaluate the results. By constructing subgraphs and knowledge paths for each sentence, we try to assign the best fitting human need to each sentence.
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@@ -34,85 +32,21 @@ Based on Maslow's pyramid and looking at the concepts from [ConceptNet](https://
[Paul&Frank(2019)](https://www.aclweb.org/anthology/N19-1368/)s approach is based on narrative texts. As dataset they use the ROCStories dataset ([Mostafazadeh, 2016]()) which contains a collection of narrative texts. We extend this approach by looking at argumentative texts and extend the procedure for our usage.
## Approach
First of all, we look for a suitable [data set](#dataset). It is important here that these are argumentative texts. The selected data set must then be prepared according to the model. To do this, we first manually annotated each of the four hundred essays from our data set with one of the maslow and one of the reiss motives. <br> After having done that we use fleiss kappa to calculate the Inter Annotator Agreement. For gold standard we then annotated 25 of each 100 essays which was annotated by another one. In sum this calculated to 75 more Annotations per person. *E.g. If I annotated essays 1-101 in the first pass, I will now annotate the first 25 of each of 102-202, 203-303, and 304-404.* In sum we then had 100 essays out of the 400 which were annotated by all four Annotators. <br> Since our selected data was already seperated into train and test, we only had to put our train and test files into correct format (see files attached). <br> For our project we only had a look at the last paragraph of each essay (more details under [data set](#dataset)). For this, we selected the last paragraph of each essay of our dataset in *Comparer.py* and compared each word in it with the concepts from ConceptNet. <br> After doing that we start with the steps equivalent to [this](https://github.com/debjitpaul/Multi-Hop-Knowledge-Paths-Human-Needs). Because some of the steps and attached filed from Debjit Pauls github were not working for us (see project_report, problems) we changed a few things, which can be taken from our README.md. After constructing the subgraphs and extracting the relevant knowledge paths we extract the human needs from the created knowledge paths and assign them to the essays (*Human_needs_assigner.py*). <br> The last thing we do is evaluate and assess our obtained results. <br> For textual analysis we wanted to use OpenFraming. But the tool is till today (march 2021) not working.
### Prerequisites
What things you need to install the software and how to install them
Give examples
### Installing
A step by step series of examples that tell you how to get a development
env running
Say what the step will be
Give the example
And repeat
until finished
End with an example of getting some data out of the system or using it
for a little demo
## Running the tests
Explain how to run the automated tests for this system
### Break down into end to end tests
Explain what these tests test and why
Give an example
### And coding style tests
Explain what these tests test and why
Give an example
## Deployment
Add additional notes about how to deploy this on a live system
## Built With
-[Contributor Covenant](https://www.contributor-covenant.org/) - Used
for the Code of Conduct
-[Creative Commons](https://creativecommons.org/) - Used to choose
the license
## Contributing
Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code
of conduct, and the process for submitting pull requests to us.
## Versioning
We use [SemVer](http://semver.org/) for versioning. For the versions
This project is licensed under the [CC0 1.0 Universal](LICENSE.md)
Creative Commons License - see the [LICENSE.md](LICENSE.md) file for
details
We chose the Student essay corpus as our data set. The dataset is provided by the [TU Darmstadt](https://www.tu-darmstadt.de/)
<br>
[This](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/argumentation_mining_1/argument_annotated_essays_version_2/index.en.jsp) is the version we used for our project. <br> The dataset consist of 404 argumentative essays. Each essay has a
## Acknowledgments
## Tools
#### ConceptNet
> ConceptNet is a freely-available semantic network, designed to help computers understand the meanings of words that people use.
ConceptNet originated from the crowdsourcing project Open Mind Common Sense, which was launched in 1999 at the MIT Media Lab. It has since grown to include knowledge from other crowdsourced resources, expert-created resources, and games with a purpose. ~ quoted from [here](https://conceptnet.io/)
- Hat tip to anyone whose code was used
- Inspiration
- etc
#### OpenFraming
> OpenFraming can perform two types of computational framing analysis: 1) unsupervised topic modeling based on Latent Dirichlet Allocation (LDA; Blei et al. (2003)), and 2) supervised learning using deep neural network Bidirectional Encoder Representations from Transformers (BERT; Devlin et al. (2018)) ~ quoted from [here](https://arxiv.org/pdf/2008.06974.pdf)
## Goals
The aim of the project is to develop a method with which human needs can also be assigned to argumentative texts and thus to carry out a meaningful analysis.
